


"""simple docstring"""


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 	snake_case		(  UpperCamelCase__							:    int      ,					UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    str="attention"     ) ->   str:
      lowerCamelCase       :       Dict   =						params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowerCamelCase       :       Tuple   =						params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowerCamelCase       :       Dict   =						params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowerCamelCase       :       Optional[int]   =						params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v




def 	snake_case		(  UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Optional[Any]=False     ) ->   Optional[int]:




      if split_mlp_wi:
            lowerCamelCase       :       List[Any]   =						params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowerCamelCase       :       Optional[int]   =						params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowerCamelCase       :       Optional[int]   =						(wi_a, wi_a)
      else:
            lowerCamelCase       :       Union[str, Any]   =						params[F'{prefix}/layers_{i}/mlp/wi/kernel']

      lowerCamelCase       :       Optional[int]   =						params[F'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo




def 	snake_case		(  UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    Tuple      ,					UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    Optional[Any]     ) ->   str:
      return params[F'{prefix}/layers_{i}/{layer_name}/scale']




def 	snake_case		(  UpperCamelCase__							:    dict      ,					*, UpperCamelCase__							:    int      ,					UpperCamelCase__							:    bool     ) ->   Any:
      lowerCamelCase       :       Any   =						traverse_util.flatten_dict(variables["""target"""]     )
      lowerCamelCase       :       Optional[int]   =						{"""/""".join(_lowerCamelCase     ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowerCamelCase       :       Union[str, Any]   =						"""encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""      ,					_lowerCamelCase     )

      lowerCamelCase       :       Any   =						collections.OrderedDict()

      # Shared embeddings.
      lowerCamelCase       :       Optional[int]   =						old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(_lowerCamelCase     ):
            # Block i, layer 0 (Self Attention).
            lowerCamelCase       :       List[str]   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""pre_attention_layer_norm"""     )
            lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       Optional[int]   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""attention"""     )
            lowerCamelCase       :       Dict   =						layer_norm
            lowerCamelCase       :       int   =						k.T
            lowerCamelCase       :       int   =						o.T
            lowerCamelCase       :       List[str]   =						q.T
            lowerCamelCase       :       Optional[int]   =						v.T

            # Block i, layer 1 (MLP).
            lowerCamelCase       :       Union[str, Any]   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					"""pre_mlp_layer_norm"""     )
            lowerCamelCase  ,      lowerCamelCase       :       str   =						tax_mlp_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""encoder"""      ,					_lowerCamelCase     )
            lowerCamelCase       :       str   =						layer_norm
            if split_mlp_wi:
                  lowerCamelCase       :       str   =						wi[0].T
                  lowerCamelCase       :       List[str]   =						wi[1].T
            else:
                  lowerCamelCase       :       Tuple   =						wi.T
            lowerCamelCase       :       Optional[Any]   =						wo.T

      lowerCamelCase       :       Dict   =						old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      lowerCamelCase       :       Dict   =						old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(_lowerCamelCase     ):
                  # Block i, layer 0 (Self Attention).
                  lowerCamelCase       :       Tuple   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_self_attention_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       int   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""self_attention"""     )
                  lowerCamelCase       :       List[Any]   =						layer_norm
                  lowerCamelCase       :       Any   =						k.T
                  lowerCamelCase       :       Any   =						o.T
                  lowerCamelCase       :       Optional[Any]   =						q.T
                  lowerCamelCase       :       Any   =						v.T

                  # Block i, layer 1 (Cross Attention).
                  lowerCamelCase       :       Optional[Any]   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_cross_attention_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase  ,      lowerCamelCase       :       Optional[int]   =						tax_attention_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""encoder_decoder_attention"""     )
                  lowerCamelCase       :       int   =						layer_norm
                  lowerCamelCase       :       List[str]   =						k.T
                  lowerCamelCase       :       Dict   =						o.T
                  lowerCamelCase       :       Any   =						q.T
                  lowerCamelCase       :       Any   =						v.T

                  # Block i, layer 2 (MLP).
                  lowerCamelCase       :       str   =						tax_layer_norm_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					"""pre_mlp_layer_norm"""     )
                  lowerCamelCase  ,      lowerCamelCase       :       Tuple   =						tax_mlp_lookup(_lowerCamelCase      ,					_lowerCamelCase      ,					"""decoder"""      ,					_lowerCamelCase     )
                  lowerCamelCase       :       Optional[Any]   =						layer_norm
                  if split_mlp_wi:
                        lowerCamelCase       :       Tuple   =						wi[0].T
                        lowerCamelCase       :       Tuple   =						wi[1].T
                  else:
                        lowerCamelCase       :       Union[str, Any]   =						wi.T
                  lowerCamelCase       :       int   =						wo.T

            lowerCamelCase       :       Optional[int]   =						old["""decoder/decoder_norm/scale"""]
            lowerCamelCase       :       int   =						old[
                """decoder/relpos_bias/rel_embedding"""
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  lowerCamelCase       :       Dict   =						old["""decoder/logits_dense/kernel"""].T

      return new




def 	snake_case		(  UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    bool     ) ->   str:
      lowerCamelCase       :       Union[str, Any]   =						collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            lowerCamelCase       :       Any   =						state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowerCamelCase       :       Union[str, Any]   =						state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""     )
                  lowerCamelCase       :       List[Any]   =						state_dict["""shared.weight"""]

      return state_dict




def 	snake_case		(  UpperCamelCase__							:    List[str]      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Optional[Any]      ,					UpperCamelCase__							:    List[Any]     ) ->   List[str]:
      lowerCamelCase       :       Dict   =						checkpoints.load_tax_checkpoint(_lowerCamelCase     )
      lowerCamelCase       :       str   =						convert_tax_to_pytorch(_lowerCamelCase      ,					num_layers=config.num_layers      ,					is_encoder_only=_lowerCamelCase     )
      lowerCamelCase       :       List[str]   =						make_state_dict(_lowerCamelCase      ,					_lowerCamelCase     )
      model.load_state_dict(_lowerCamelCase      ,					strict=_lowerCamelCase     )




def 	snake_case		(  UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    bool = False     ) ->   Optional[Any]:
      lowerCamelCase       :       Optional[int]   =						TaConfig.from_json_file(_lowerCamelCase     )
      print(F'Building PyTorch model from configuration: {config}'     )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            lowerCamelCase       :       Optional[Any]   =						TaEncoderModel(_lowerCamelCase     )
      else:
            lowerCamelCase       :       str   =						TaForConditionalGeneration(_lowerCamelCase     )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase     )

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'     )
      model.save_pretrained(_lowerCamelCase     )

      # Verify that we can load the checkpoint.
      model.from_pretrained(_lowerCamelCase     )
      print("""Done"""     )


if __name__ == "__main__":
    __lowerCamelCase   :int						     =		argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
    )
    __lowerCamelCase   :List[Any]						     =		parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )






"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer



					class 			lowercase					( lowercase__		):






								def __init__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	int = 101			)       ->	List[str]:





									"""simple docstring"""


									lowerCAmelCase												=  length






								def __len__(self  :	Any			)       ->	List[Any]:





									"""simple docstring"""


									return self.length






								def __getitem__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





									"""simple docstring"""


									return i





					class 			lowercase					:






								def __call__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





									"""simple docstring"""


									return {"input_ids": torch.tensor(SCREAMING_SNAKE_CASE_			), "labels": torch.tensor(SCREAMING_SNAKE_CASE_			)}





					class 			lowercase					( nn.Module		):






								def __init__(self  :	List[str]			)       ->	List[Any]:





									"""simple docstring"""


									super().__init__()
									# Add some (unused) params otherwise DDP will complain.
									lowerCAmelCase												=  nn.Linear(120      ,80			)






								def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None			)       ->	Union[str, Any]:





									"""simple docstring"""


									if labels is not None:
										return torch.tensor(0.0      ,device=input_ids.device			), input_ids
									else:
										return input_ids



class 			lowercase					( lowercase__		):






			@require_torch_neuroncore
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call





class 			lowercase					( lowercase__		):






			@require_torch_multi_gpu
			def 					UpperCAmelCase							(self  :	List[str]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
				lowerCAmelCase												=  F"""--output_dir {output_dir}""".split()
				lowerCAmelCase												=  ['''torchrun'''] + distributed_args + args
				execute_subprocess_async(SCREAMING_SNAKE_CASE_      ,env=self.get_env()			)
				# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					UpperCAmelCase        =				HfArgumentParser((TrainingArguments,))
					UpperCAmelCase        =				parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
					    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [1_0_1, 4_0, 7]:
										UpperCAmelCase        =				DummyDataset(dataset_length)




										def __magic_name__	(				_lowerCamelCase:		EvalPrediction			)					->						Dict:

											'''simple docstring'''




											lowerCAmelCase												=  list(range(len(_lowerCamelCase			)			)			)
											lowerCAmelCase												=  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
											if not success and training_args.local_rank == 0:
												logger.warning(
												    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
												    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""			)
											return {"success": success}

										UpperCAmelCase        =				Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										UpperCAmelCase        =				2

										UpperCAmelCase        =				trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										UpperCAmelCase        =				trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)



										UpperCAmelCase        =				None


